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Found 8 papers, 1 papers with code
$\mathbb{X}$Resolution Correspondence Networks
In this paper, we aim at establishing accurate dense correspondences between a pair of images with overlapping field of view under challenging illumination variation, viewpoint changes, and style differences.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
Pose Guided RGBD Feature Learning for 3D Object Pose Estimation
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation.
A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth
The iterative refinement is accomplished based on finer (smaller) parts that are represented with more discriminative control point descriptors by using our Iterative Hough Forest.
Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art.
Iterative Hough Forest with Histogram of Control Points for 6 DoF Object Registration from Depth Images
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space.
Latent-Class Hough Forests for 6 DoF Object Pose Estimation
In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios.
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly.
